Neural architecture search (NAS) is a popular solution to deep learning model selection [1]}, [2]}, [3]}, [4]}, which automatically selects a good neural network architecture for a given learning task.
Since an overly complex model may take too-long training time and thus may become a serious obstacle of neural architecture search [3]}, [2]}, the accuracy-complexity tradeoff is an important consideration in neural architecture search.
Liu et al. [2]} propose Progressive Neural Architecture Search, which searches for convolutional neural network architectures in the increasing order of model complexity. Therefore, Progressive Neural Architecture Search favors low complexity models that meet the requirement on prediction accuracy.
Laredo et al. [3]} propose Automatic Model Selection, which searches for fully connected neural networks that yield a good balance between prediction accuracy and model complexity.
